Abstract
The mining area is the main place for the development and utilization of Coalbed Methane (CBM), and there are a series of systems for the development and utilization of CBM. However, owing to lack of a clear understanding of demand-side gas consumption rules and a reasonable resource allocation system, a large amount of CBM resources in the mining area are wasted. In order to predict the demand for CBM dynamically, the Seasonal Auto Regressive Integrated Moving Average (SARIMA) model, Additive Holt-Winters (AHW) model and Multiplicative Holt-Winters (MHW) model based on time series are used to predict the monthly demand for CBM in Yangquan Mine Area in 2020, respectively. Then the predicted results are evaluated by using the prediction model parameters combined with the characteristics of actual demand for CBM. Finally, a resource allocation system under different supply and demand conditions is built to reduce the waste of resources. In this paper, it is found that the information of the actual data is not sufficiently extracted in the MHW model while the SARIMA model can reflect the cyclical trend of monthly demand for CBM under ideal conditions. Furthermore, the AHW model can reasonably predict the demand for CBM under the influence of COVID-19, with a mean relative error of 0.099. The supply and demand distribution system built based on the proposed models can solve the problem of seasonal unevenness of CBM demand in mining areas and ensure the economic benefits of mining areas.
Highlights
Coal occupies an important position in the world’s energy consumption structure, and gas disasters are an important limiting factor that endangers the safe production in coal mining countries (Tutak and Brodny, 2019a, 2019b; Wang et al, 2019, 2020)
In order to predict the demand for Coalbed Methane (CBM) more accurately and formulate a reasonable resource allocation plan, the Seasonal Auto Regressive Integrated Moving Average (SARIMA) prediction model, Additive Holt-Winters (AHW) model, and Multiplicative Holt-Winters (MHW) model are used to predict the monthly demand for CBM in Yangquan Mine Area in 2020 with the seasonality of the CBM demand taken into account
This study considers three modes of transportation costs, with transportation costs as the objective function, while ensuring the economic and social benefits of the mining area, and constructing a reasonable CBM resource allocation model
Summary
Coal occupies an important position in the world’s energy consumption structure, and gas disasters are an important limiting factor that endangers the safe production in coal mining countries (Tutak and Brodny, 2019a, 2019b; Wang et al, 2019, 2020). To take seasonal and periodic factors into account, time series models are adopted to predict the demand for CBM. The basic idea of the ARIMA model is: the data series formed by the predicted object over time can be regarded as a random series probably including volatility, periodic trends and seasonal factors, and after technical processing a certain mathematical model can be used to describe this series approximately (Deb et al, 2017). The basic idea of the Holt-Winters exponential smoothing model is: make smooth estimates of long-term trends, trend increments, and seasonal changes for the time series with linear trends, seasonal changes, and random changes, and build a prediction model and extrapolate the predicted values (Ferbar Tratar and Strmcnik, 2016; Makananisa and Erero, 2018) This method can handle both trends and seasonal changes, and it can filter out the effects of random fluctuations appropriately. The AHW model and MHW model are constructed by the software SPSS, and the three-parameter additive and multiplicative exponential smoothing model is used to predict the CBM demand from January to December in 2020
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